Shrimp processing system and methods

ABSTRACT

Methods and systems using a vision system to process shrimp. The vision system captures images of samples of shrimps. The processor produces a digital image of the shrimps in the samples. Shrimps exiting a peeler are imaged to determine the number of tail segments in each. The shrimps are classified by the number of intact segments, and quality, yield, and throughput computed from the classification results. The processor can control operational settings of the peeler based on the classification results. In a larger system including other shrimp-processing equipment besides the peeler, other points along the processing path can be imaged by camera or sensed by other sensors to determine processing quality and to make automatic operational adjustments to the equipment.

BACKGROUND

The invention relates to apparatus and methods for processing shrimp.

Originally introduced because of the high labor costs associated withpeeling small shrimp by hand, shrimp-peeling machines are now widelyused in the shrimp processing industry. Roller-type shrimp-peelingmachines, in particular, dominate the bulk peeling industry. U.S. Pat.Nos. 2,778,055 and 2,537,355, both to Fernand S., James M., and Emile M.Lapeyre, describe the basic structure and principles of operation ofroller-type shrimp peelers.

Many factors affect the throughput, quality, and yield of peeled shrimp.Some factors related to the shrimp themselves include the species, size,uniformity, and freshness of the shrimp. Factors related to the peelingequipment, include the feed rate of shrimp to the peeler, water flow tothe peeler, and finger-frame pressure. Other factors relate to othershrimp-processing equipment, such as cleaners, shrimp feed systems,roller separators, air separators, and graders. The equipment-relatedfactors are generally manually adjustable to improve peeling quality andyield for a given batch of shrimp or to compensate for peeling-rollerwear. Because the quality and yield of the peeled shrimp directly affecttheir production cost and the price they can command, proper adjustmentof the peeling equipment is important. But proper manual adjustmentrequires diligent monitoring of the output quality and yield andexperience in selecting the adjustments that should be made.

SUMMARY

One version of a method embodying features of the invention forprocessing peeled shrimps includes: (a) distributing deheaded, peeledshrimps onto a support surface; (b) creating a digital image of theshrimps on the support surface; (c) determining the number of tailsegments present in each of the shrimps from the digital image; and (d)classifying each of the shrimps into one of a plurality of classesaccording to the number of tail segments present in each of the shrimps.

A shrimp-processing system embodying features of the invention comprisesa shrimp peeling machine removing the heads and shells from shrimp toproduce peeled shrimps. A conveyor conveys the peeled shrimps from theshrimp peeling machine to downstream processing. A vision systemcaptures a digital image of peeled shrimps on the conveyor. A processordetermining the number of tail segments present in each of the shrimpsfrom the digital image and classifies each of the shrimps into one of aplurality of classes according to the number of tail segments present ineach of the shrimps.

Another shrimp-processing system embodying features of the inventioncomprises a peeler for removing the shells and heads from unpeeledshrimps to produce peeled shrimps. A feed system feeds unpeeled shrimpsto the peeler. A conveyor system conveys peeled shrimps from the peelerdownstream along a process path. One or more shrimp-processing machinesdownstream of the peeler in the process path act on the peeled shrimps.One or more sensors disposed along the process path detect one or moreoperational variables of the shrimp-processing machines or of the peeleror physical characteristics of the shrimp and produce sensor signalsindicative of the one or more operational variables. A processorreceives the sensor signals and derives control signals from the sensorsignals. The control signals are sent to the peeler or the one or moreshrimp-processing machines to adjust one or more operational settings

BRIEF DESCRIPTION OF THE DRAWINGS

These features and aspects of the invention are described in more detailin the following description, appended claims, and accompanyingdrawings, in which:

FIG. 1 is a pictorial view of a peeled shrimp;

FIGS. 2A-2C are side views of high quality, medium quality, and lowquality peeled shrimps; and FIG. 2D is a side view of a shrimp bit;

FIG. 3 is a block diagram of a portion of an automated shrimp-peelingsystem embodying features of the invention;

FIG. 4 is a graph showing the relationship between the cumulativepercentage of a peeled shrimp's total weight and the number of intactcontiguous tail segments;

FIG. 5 is a block diagram of an automated shrimp-processing systemembodying features of the invention;

FIG. 6 is a flowchart of one version of a control scheme usable in anautomated shrimp-peeling system as in FIG. 3; and

FIG. 7 is a flowchart of one version of a weight-based algorithm forestimating yield and throughput in a shrimp-peeling system as in FIG. 3.

DETAILED DESCRIPTION

FIG. 1 shows the anatomy of a peeled, or shelled and deheaded, shrimp.The complete shrimp meat 10 includes six main segments S1-S6 and atelson T. The head-end segment S1 has the largest girth of all thesegments; the tail-end telson T is sometimes lost along with the shellin the peeling process. The segments S1-S6 are typically referred to as“tail segments.” Borders B between adjacent tail segments are generallydiscernable, as are other features, such as indentations I along theback and indentations J along the underside at the junctions of theadjacent tail segments.

One way to determine the quality of the peel is by counting the numberof contiguous tail segments of the peeled shrimps exiting a peeler andclassifying each peeled shrimp as High Quality, Medium Quality, or LowQuality. For example, each shrimp having six full tail segments S1-S6(with or without all of its telson) could be classified as High Quality(FIG. 2A); each shrimp not of High Quality and having complete tailsegments S1-S5 plus more than half of tail segment S6 (FIG. 2B) could beclassified as Medium Quality; and every shrimp not of High or MediumQuality and having complete tail segments S1-S5 could be classified asLow Quality (FIG. 2C). Shrimps having fewer tail segments than the LowQuality shrimp could be classified along with shrimps missing the firsttail segment S1 as Bits. Of course, the quality levels, or classes, maybe defined in other ways. For example, the Medium Quality class couldinclude all shrimp not of High Quality and having five full tailsegments (S1-S5); the Low Quality class could include all shrimps not ofHigh or Medium Quality and having four full tail segments (S1-S4); andany other shrimp meats could be classified as bits. Alternatively, morethan four quality levels could be defined.

An automated peeling system embodying features of the invention is shownin FIG. 3. A shrimp peeling machine, or peeler, 12 removes the heads andshells from shrimps. One example of such a peeler is the Model A peelermanufactured and sold by Laitram Machinery, Inc. of Harahan, La., U.S.A.The general structural details and operation of a roller-type peelerlike the Model A is described in U.S. Pat. No. 2,778,055, which isincorporated by reference into this description. Shrimp meats 14 aretransported out of the peeler 12 in a conveying direction 15 to furtherprocessing stations downstream on a conveyor 16, such as a conveyor belthaving an outer conveying surface 18, which supports the shrimp.

A vision system 20 including one or more cameras 22 captures a frameimage of the shrimps on a portion of the conveyor. The vision system 20produces digital images of the shrimps 14 on the conveying surface 18.The shrimps generally rest side-down on the conveying surface 18, whichmay be a darker surface than the shrimp meat to provide contrast forbetter imaging. The digital image of the frame is sent to a processor24, which processes the image. Imaging algorithms detect physicalcharacteristics, or features, of the shrimp, such as, for example, outerupper and lower edges showing the indentations (I, J; FIG. 1) and moreheavily pigmented lines of high contrast in the shrimp meat indicatingboundaries (B; FIG. 1) between tail segments. As an alternative todiscerning the tail segments from boundaries or indentations, imagingalgorithms using a shrimp's projected area, its perimeter, its arclength, or relationships between various dimensions, such as the ratioof the width of the shrimp (upper edge to lower edge) at the head end inthe first tail segment S1 to the width of the last intact tail segment,could be used to determine the quality class. Or a pattern-recognitionalgorithm that compares each imaged shrimp to standard digital models ofshrimps of various numbers of tail segments could be used. A library ofstandard digital images of various species of shrimp could bemaintained. From the processed image, the algorithms determine thenumber of contiguous intact tail segments for each shrimp image in theframe and classify each shrimp into one of the quality classes. Thealgorithms also count the total sample of shrimps and the number ofshrimps in each quality class for each frame. From the counts, thealgorithms compute peeling quality and yield statistics, such as theyield of each quality class, which can be reported on a display monitor26, printed as a report, or sound an alarm if the statistics lie outsidepreset limits. The processor 24 can also adjust one or more operatingparameters of the peeler, such as infeed rate, roller rotationfrequency, finger-frame pressure, and water flow, in response toout-of-limits yield or quality values by control signals over controllines 27.

One exemplary version of a control scheme usable with the system of FIG.3 is shown in the flowchart of FIG. 6. First, the visioning systemcaptures a frame 30 of a sample of the peeled shrimps after they exitthe peeler. A digital image of each shrimp in the sample is then createdfrom the frame 32 and processed in one of the ways previously described.The shrimps in the frame are counted 34 to give the sample size. Fromthe digital image of each shrimp, the number of contiguous, intact tailsegments of each shrimp is determined by a tail-segment-countingalgorithm, and the shrimps are classified 36 into quality levels, e.g.,high, medium, and low, depending on the number of intact tail segments.The classification process also classifies shrimps having fewer intacttail segments than the threshold for low-quality shrimp or shrimpportions missing the first segment S1 as bits, which are usually notlarge enough to be sold as peeled shrimp. Shrimps that include pieces ofshell may also be counted. The relatively greater reflectivity of shellversus that of shrimp meat can be used in the image processing to detectincompletely peeled shrimps with residual shell. The yield in eachquality class, i.e., the quality yield, is computed 38 as: Bits %=bitscount/sample size; Low Quality %=Low Quality count/sample size; MediumQuality %=Medium Quality count/sample size; and High Quality %=HighQuality count/sample size. These percentage values provide a measure ofyield based on count, which does not require measuring or estimating theweight of the shrimp. The peeling quality is calculated 40 as: PeelingQuality %=1−(shell-on count/sample size). To smooth the computed valuesfrom frame to frame, the computed values or the individual counts can befiltered 42 in a moving-average digital filter, for example. Then thefiltered values can be displayed 44 or otherwise reported. The filteredquality values can also be used by the processor to automatically adjustoperating parameters 46 of the peeler. The process is then repeated foranother frame.

The process described with respect to the flowchart of FIG. 6 computesquality classes and yield values based on relative counts of shrimps inthe various quality levels, categories, or classes, that are defined bythe number of contiguous intact tail segments. Another way yield can bemeasured is by converting the tail-segment count into a shrimp weight.The relationship between the percent cumulative weights of peeled shrimpmeat from the head-end segment S1 through the telson T has beenempirically determined for three particular groups of shrimp in FIG. 4.The three graphed curves are for wild, cold-water Pandalus Borealis andfor Vannamei farm-raised in Honduras and Peru. For Pandalus Borealis,the first segment represents just under 30% of the weight of a completepeeled shrimp, the first and second segments together represent about45%, and the first through sixth segments represent about 98% of theshrimp by weight. Using the tail-segment-counting algorithm to determinethe number of intact tail segments, including fractions of the finalincomplete segment, the processor can use the predetermined curve forthe kind of shrimp being peeled to assign a percentage-of-full-weightestimate to each piece of shrimp meat. Then the percentages of all theshrimps in the frame can be averaged to obtain a weight-based yieldpercentage for each frame, which could be filtered in the same way asthe yield values in FIG. 6.

Other vision-system algorithms can alternatively be used to determineeach shrimp's volume. In a one-camera, two-dimensional image capture,the shrimp's projected area, its perimeter, its arc length, or otherdimensional attributes, which may be used to determine the segmentcount, could also be used to estimate the shrimp's volume. Withknowledge of the meat density (weight/volume) of the species of shrimpbeing measured, the weight of each shrimp piece is determined bymultiplying the volume by the density. An exemplary weight-basedalgorithm for calculating yield and throughput is depicted in theflowchart of FIG. 7. After a frame image of a peeled shrimp sample iscaptured 30 and a digital image of each shrimp in the sample is created32, a shrimp's volume is determined and the number of tail segments ofthe shrimp is counted 80. The shrimp is then classified 36 according topre-established classification criteria. If the shrimp is classified asa bit 82, the next shrimp in the frame is analyzed in the same way. Ifthe shrimp is not a bit and is classified into one of the qualitylevels, its weight is estimated 84 from its volume and meat density, asdescribed previously. The number of intact tail segments of the shrimpis used to determine what percentage of its full weight is present 86 byreference to the %-weight versus number-of-segments curve as in FIG. 4.The algorithm uses the %-weight value to estimate the full-segment orsix-segment weight 88 of the shrimp as though all its tail segments S1-Twere intact. The computed weight of the imaged shrimp is divided by the%-weight value from the curve to compute the six-segment weight.Multiple cameras can be used to create a three-dimensional image and amore direct measurement of volume to be used to determine weight.

Some of the missing segments exit the process upstream of the cameraposition. For example, some of the missing segments are pulled throughthe peeler rollers and discarded. But other of the segments missing fromthe shrimps are conveyed to the vision system and imaged. That's whythose missing segments, or bits, if they are not going to be sold, arenot counted in calculating yield or throughput. The calculated weight ofeach non-bit shrimp piece is added to the accumulated weights for itsclass 90. The six-segment weights are summed 92 as well to compute arunning six-segment total weight; i.e., what the accumulated weight ofthe peeled shrimp in the sample would be if all the shrimp had their sixsegments intact. The yield is computed 94 for each class by dividing theaccumulated weights for each class by the accumulated six-segment weightof the sample. The throughput of the sample of peeled shrimp by classand overall is derived 96 by dividing the accumulated weights for eachclass and the six-segment weight by the time interval represented by thesample. The yield and throughput computations do not have to beperformed at the same rate as the weight-summing, which is performed aseach shrimp is analyzed. For example, the yield and throughputcalculations could be performed only once per frame and could befiltered with previous values as described with reference to FIG. 6.

Thus, the vision system just described is based on estimated shrimpweights rather than on shrimp counts. The vision system identifies whichsegments are missing from individual shrimps to determine the qualitylevel of each shrimp (from its number of intact segments). Then one ormore segment-weight algorithms (using empirically determined curves asin FIG. 4) and density algorithms are used to compute each peeledshrimp's percentage and absolute loss in weight due to its missingsegments. From that information, the %-yields and throughputs for eachquality level and overall is computed.

The accuracy of the weight-based methods can be improved by the additionof an in-line, real-time weight measurement provided by a weighingdevice 28 (FIG. 3), such as a hopper weigh scale or a dynamic weighbelt. The weighing device sends a weight measurement to the processor 24over a signal line 29. The weight measurement is used to refine theaccuracy of the computed throughput values, which also improves theaccuracy of the %-yield values.

The visioning system with its tail-segment-counting, throughput, yield,and other algorithms can be integrated into a larger automatedshrimp-processing system as shown in FIG. 5. The exemplaryshrimp-processing system 50 includes one or more peelers 12 andassociated vision systems 20 imaging peeled shrimp as previouslydescribed. Shell-on shrimp are drawn into the system by a pump orconveyor 52 and delivered to a rock-tank system 54, in which rock,shell, and other debris are removed from the shrimp by turbulent waterflow in a rock tank. The shrimp separated from the debris are collectedin the rock-tank system's receiving tank and pumped to a feed tank 56.The shrimp are conveyed from the feed tank by an automated feed system58 that distributes the shrimp to the peelers 12. The peeled shrimp areconveyed from the peelers to one or more cleaners 60, which detach anyresidual shell and waste material. The shrimp are then conveyed to oneor more roller separators 62 that separate the waste material detachedby the cleaners 60 from the shrimp. Air separators 64 separate loosenedwaste material and shell from the shrimp. The shrimp are then conveyedon an inspection belt 66 where the shrimp can be inspected before beinggraded into different sizes in one or more graders 68.

A global control processor 70 is used to monitor and control the entireshrimp-processing system 50. The global control processor can berealized as a single central processor or a network of distributedprocessors. The global control processor receives image data over inputlines 71 from the peeler-output visioning systems 20 and other visioningsystems 72 positioned at various points along the shrimp's process paththrough the system. The processor 70 can receive sensor signals fromother sensors measuring other system variables, such as temperatures,weights, and speeds. For example, shrimp entering the peeler 12 can beimaged to determine if the throughput is too high. The output of thecleaners 60 can be monitored to determine the quality of the cleaningprocess. Likewise, the qualities of the roller-separation andair-separation processes can be determined by monitoring the outputs ofthe roller separators 62 and the air separators 64. The inspection belts66 can be monitored to check on the infeed rate of shrimp to the graders68. Besides the Model A peeler, Laitram Machinery, Inc. manufactures andsells other shrimp-processing equipment, such as the Laitram® AutomatedFee System, the Model RTFS Rock Tank and Feed System, the Model CCleaner, the Model S Roller Separator, the Model AS Air Separator, theModel IB Inspections Belt, and the Model G-8 Grader. Equipment such asthe Laitram Machinery equipment mentioned is outfitted with actuatorsthat can adjust various operational parameters of the equipment inresponse to control signals. The processing-equipment stationsdownstream of the peeler are linked by a conveyor system that mayinclude conveyor belts, elevators, fluid conduits, or other transportapparatus transporting shrimps along the process path. The globalcontrol processor 70 runs algorithms and routines that develop shrimpimages from the vision data and compute throughput, quality, and yieldresults at various points in the shrimp-processing system. The resultscan be displayed and used to derive control signals to automaticallycontrol the operation of the system over processor control output lines74 to improve quality and yield. For example, the processor 70 cancontrol the rate of delivery of shrimp to the rock tank 54 bycontrolling the speed of the input pump 52 or conveyor. The processorcan also control the rate of delivery of shrimp from the rock-tanksystem's receiving tank to the feed tank 56 by controlling the speed ofthe receiving tank's pump. Both of these adjustments may depend on thevolume of shrimp in the feed system 58 being distributed to the peelers12. The volume of shrimp or their feed rate, or throughput, isdetermined from the vision system or from weigh scales. If thethroughput is too high, the infeed pumps can be slowed and the speed ofconveyor belts in the feed system can be slowed. If the throughput istoo low, it can be increased by increasing the speeds of the pumps andthe conveyor belts. Just like the processor 24 in FIG. 3, the globalcontrol processor 70 can control the water flow, the finger-framepressure, the roller pressure, or the roller-revolution rate based onthe peeling quality, throughput, and yield measures. The water flow orwheel speed of the cleaners 60 can be adjusted based on the quality ofthe cleaning process. The water flow or the roller spacing of the rollerseparators 62, the fan speeds of the air separators 64, the speeds ofthe inspection belts 66, and the grade settings of the graders 68 canall be adjusted automatically as a function of the quality, yield, andthroughput values computed from the vision or other measurements madethroughout the system. And the global control processor 70 can monitorand control other system components, such as a waste water managementsystem 76 and the temperature of the water. Or the processor can controla robotic culling system 78 at the inspection belt 66, for example, tosegregate, unwanted shrimp bits and unidentifiable items from desirableshrimp product or to grade the shrimp instead of relying on a separategrader 68.

What is claimed is:
 1. A processor-implemented method for processingpeeled shrimps, comprising: depositing deheaded, peeled shrimps receivedfrom a peeling machine onto a support surface; creating a digital imageof the shrimps on the support surface with a vision system; sending thedigital image to a processor; estimating, by the processor, thepercentage of full weight of each of the shrimps from the digital image;classifying, by the processor, each of the shrimps into one of aplurality of classes according to the percentage of full weight of eachof the shrimps; compiling, by the processor, peeling statistics from theestimates of percentage of full weight of the shrimps; and adjusting oneor more operational settings of the peeling machine based on the peelingstatistics.
 2. The method of claim 1 wherein the shrimps are depositedside down on the support surface.
 3. The method of claim 1 furthercomprising: counting, by the processor, the number of shrimps classifiedin each of the plurality of classes.
 4. The method of claim 3comprising: transporting shrimps on the support surface continuously ina conveying direction; creating a series of digital images of differentgroups of the shrimps on the support surface with a vision system;accumulating, by the processor, the counts of the numbers of shrimps ineach of the classes from the series of digital images.
 5. The method ofclaim 4 further comprising: calculating, by the processor, a movingaverage of the count for each class with a smoothing filter.
 6. Themethod of claim 1 further comprising: counting, by the processor, thenumber of shrimps in the digital image.
 7. The method of claim 1comprising: estimating the percentage of full weight of each of theshrimps by determining the number of tail segments present in each ofthe shrimps from the digital image; classifying each of the shrimps intoone of four classes, wherein a first class includes shrimps having sixfull segments, a second class includes shrimps not in the first classhaving five and a half or more segments, a third class includes shrimpsnot in the first or second class having five full segments, and a fourthclass includes shrimps not in the first, second, or third class.
 8. Themethod of claim 3 further comprising: calculating, by the processor,yield quality for each of the classes as the ratio of the count ofshrimps in each class to the sum of the counts in all the classes. 9.The method of claim 8 further comprising: reporting the yield quality ofeach of the classes.
 10. The method of claim 3 further comprising:determining, by the processor, the count of shrimps with attached shellfrom the digital image.
 11. The method of claim 10 further comprising:calculating, by the processor, peeling quality as one minus the count ofshrimps with shell attached to the sum of the counts in all the classes.12. The method of claim 11 further comprising: reporting the peelingquality.
 13. The method of claim 1 comprising: estimating the percentageof full weight of each of the shrimps by determining the number of tailsegments present in each of the shrimps by comparing a digital image ofeach of the shrimps to a digital model associated with each of theclasses and determining the best match of the digital image to thedigital model to classify each of the shrimps.
 14. The method of claim 1comprising: estimating the percentage of full weight of each of theshrimps by determining the number of tail segments present in each ofthe shrimps from the arc of the shrimp from a head end to an oppositetail end.
 15. The method of claim 14 wherein the number of tail segmentsis determined from the length of the arc.
 16. The method of claim 14wherein the number of tail segments is determined from the angularextent of the arc.
 17. The method of claim 1 comprising: estimating thepercentage of full weight of each of the shrimps by determining thenumber of tail segments present in each of the shrimps from the ratio ofthe width of the shrimp at a wider head end to the width of the shrimpat a narrower opposite tail end.
 18. The method of claim 1 comprising:estimating the percentage of full weight of each of the shrimps bydetermining the number of tail segments present in each of the shrimpsfrom pigmentation lines extending from the upper edge to the lower edgeof the shrimp at the interface between contiguous segments.
 19. Themethod of claim 1 comprising: estimating the percentage of full weightof each of the shrimps by determining the number of tail segmentspresent in each of the shrimps from indentations detected in the upperand lower edges of the shrimp.
 20. The method of claim 1 comprising:creating the digital image of the shrimps on the support surfacedisposed downstream of a peeling machine that peeled and deheaded theshrimps; counting, by the processor, the number of shrimps classifiedinto each of the plurality of classes; calculating, by the processor,yield quality for each of the classes as the ratio of the count ofshrimps in each class to the sum of the counts in all the classes;adjusting one or more operational settings of the peeling machine as afunction of the calculated yield quality for each of the classes. 21.The method of claim 1 comprising: creating the digital image of theshrimps on the support surface disposed downstream of a peeling machinethat peeled and deheaded the shrimps; counting, by the processor, thetotal number of shrimps in the digital image; counting, by theprocessor, the number of shrimps in the digital image with shellattached; calculating, by the processor, peeling quality as one minusthe count of shrimps with shell attached to the total number of shrimpsin the digital image; adjusting one or more operational settings of thepeeling machine as a function of the calculated peeling quality.
 22. Ashrimp-processing system comprising: a shrimp peeling machine removingthe heads and shells from shrimp to produce peeled shrimps; a conveyorconveying the peeled shrimps from the shrimp peeling machine todownstream processing; a vision system capturing a digital image ofpeeled shrimps; a processor determining the number of tail segmentspresent in each of the shrimps from the digital image and classifyingeach of the shrimps into one of a plurality of classes according to thenumber of tail segments present in each of the shrimps.
 23. Ashrimp-processing system as in claim 22 wherein the processor furthercomputes yield values for each of the classes.
 24. A shrimp-processingsystem as in claim 23 wherein the processor further sends controlsignals derived from the yield values to the shrimp peeling machine toadjust one or more operational settings of the shrimp peeling machine.25. A shrimp-processing system comprising: a shrimp peeling machineremoving the heads and shells from shrimp to produce peeled shrimps; aconveyor conveying the peeled shrimps from the shrimp peeling machine todownstream processing; a vision system capturing a digital image ofshrimps peeled by the shrimp peeling machine; a processor determiningthe percentage of full weight of each of the imaged peeled shrimps fromthe digital image and classifying each of the imaged peeled shrimps intoone of a plurality of classes according to the percentage of full weightof each of the imaged peeled shrimps.
 26. A shrimp-processing system asin claim 25 comprising: one or more shrimp-processing machinesperforming the downstream processing; one or more sensors detecting oneor more operational variables of the shrimp-processing machines or ofthe shrimp peeling machine or physical characteristics of the shrimp andproducing sensor signals indicative of the one or more operationalvariables; the processor receiving the sensor signals and derivingcontrol signals from the sensor signals and sending the control signalsto the shrimp peeling machine or the one or more shrimp-processingmachines to adjust one or more operational settings.
 27. Ashrimp-processing system as in claim 25 wherein the vision systemcaptures a digital image of shrimps on the conveyor.
 28. The method ofclaim 1 further comprising: estimating, by the processor, the weight andthe six-segment weight of each of the shrimps from the digital image;summing, by the processor, the estimated weights of each of the shrimpsto compute an accumulated weight; summing, by the processor, theestimated six-segment weights of each of the shrimps to compute anaccumulated six-segment weight; computing, by the processor, the yieldby dividing the accumulated weight by the accumulated six-segmentweight.
 29. The method of claim 1 wherein the support surface comprisesa conveyor for conveying the shrimp along a process path.
 30. Ashrimp-processing system as in claim 22 wherein the vision systemcaptures a digital image of peeled shrimps on the conveyor.